Black-Box Reductions for Parameter-Free Online Learning in Banach Spaces
Abstract
We introduce several new black-box reductions that significantly improve the design of adaptive and parameter-free online learning algorithms by simplifying analysis, improving regret guarantees, and sometimes even improving runtime. We reduce parameter-free online learning to online exp-concave optimization, we reduce optimization in a Banach space to one-dimensional optimization, and we reduce optimization over a constrained domain to unconstrained optimization. All of our reductions run as fast as online gradient descent. We use our new techniques to improve upon the previously best regret bounds for parameter-free learning, and do so for arbitrary norms.
Cite
Text
Cutkosky and Orabona. "Black-Box Reductions for Parameter-Free Online Learning in Banach Spaces." Annual Conference on Computational Learning Theory, 2018.Markdown
[Cutkosky and Orabona. "Black-Box Reductions for Parameter-Free Online Learning in Banach Spaces." Annual Conference on Computational Learning Theory, 2018.](https://mlanthology.org/colt/2018/cutkosky2018colt-black/)BibTeX
@inproceedings{cutkosky2018colt-black,
title = {{Black-Box Reductions for Parameter-Free Online Learning in Banach Spaces}},
author = {Cutkosky, Ashok and Orabona, Francesco},
booktitle = {Annual Conference on Computational Learning Theory},
year = {2018},
pages = {1493-1529},
url = {https://mlanthology.org/colt/2018/cutkosky2018colt-black/}
}